Broadband wireless communications systems have become an integral part of the global communications infrastructure with the rapid growth in popularity of wireless data services. There remains a need for developing new techniques for better channel utilization due to the limited bandwidth resources of wireless communications systems.
In wide-band digital communications systems, modulation pulses will spread and result in inter-symbol interference (ISI) when modulation bandwidth exceeds the coherence bandwidth of the radio channel. Typically, equalization algorithms are built into the receiver to compensate for channel amplitude and delay variations and combat ISI for reducing bit error rate (BER). Generally, the equalization algorithms can be categorized into training-based equalization and blind equalization.
Receivers utilizing training-based equalization algorithms typically include channel estimators having adaptive filters. Adaptive filters include coefficient parameters that can be adjusted, or trained, in dependence upon the characteristics of a received signal. The adjustment of the filter coefficient parameters is accomplished by transmitting a known training sequence of symbols to the receiver. The adjustment of the filter coefficient parameters is effected by comparing the received symbols to the known transmitted symbols, so as to minimize the differences between the received and transmitted symbols. This adjustment is termed equalization, because it has the effect of reducing, or equalizing, the effects of those environmental sources which caused the observed errors. After the adjustment, or training, of the receiver, the transmission of message symbols can commence. The underlying assumption in this scenario is that the environmental conditions which caused differences in the received training symbols, compared to the transmitted training symbols, would affect the subsequent received message symbols as well, and, therefore, an adjustment to the filters which minimized the errors in the received training symbols would also minimize errors in the received message symbols. Typically, training-based equalization is implemented periodically due to the time-varying nature of the wireless channel.
In blind equalization algorithms, training is not needed and higher bandwidth utilization may be achieved because the channel can be fully devoted to data packet transmission. Blind equalization is more complicated than periodic training equalization, and the performance of blind equalization suffers from a slower convergence rate. On the other hand, the bandwidth utilization of periodic training equalization is lower due to the requirement of training sequences. Additionally, a careless selection of training interval can result in either redundant training sequences when the channel varies relatively slowly, or excessive packet retransmissions when the channel varies relatively fast.
Some current training-based equalization algorithms include a scheme for determining intervals for initiating a training sequence. The basic idea of the scheme is that no training sequence is transmitted until the abrupt change detection algorithm detects changes in channel parameters that may cause an equalizer failure. In such case, the receiver requests the transmitter to transmit the training sequence to re-adjust the channel estimations at the receiver so as to recover from the failures. This scheme is known as condition-based training because the training decision is based on the channel conditions. However, this scheme is constrained by the complexity of implementation of the abrupt change detection algorithm and may be prone to performance degradation due to false and missed alarms.
Communications systems would benefit by having a scheme for determining a training decision including reduced algorithm complexity. Additionally, communications systems would benefit by having a training decision scheme that improves communication performance, specifically, channel utilization. Thus, it is desired to provide a training decision scheme having reduced complexity and improved channel utilization.